Intelligent optimization device for AI auxiliary radiotherapy dose

The AI-assisted intelligent radiotherapy dose optimization device utilizes multimodal image fusion and deep learning technology to solve the problems of time-consuming target area definition and inability to dynamically adjust in traditional radiotherapy. It achieves high-precision target area delineation and individualized dose optimization, thereby improving the accuracy and safety of radiotherapy.

CN121846546APending Publication Date: 2026-04-14CANCER HOSPITAL AFFILIATED TO GUANGXI MEDICAL UNIV
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Patent Information

Application Number
CN202511905806.X
Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
Filing Date
2025-12-17
Publication Date
2026-04-14

AI Technical Summary

Technical Problem

Traditional radiotherapy suffers from subjective target definition, time-consuming dose calculation, and lack of dynamic adjustment, resulting in limited efficacy and toxicity control, and an inability to address changes in the patient's anatomical structure.

Method used

The device employs an AI-assisted intelligent optimization system for radiotherapy dosage. It eliminates respiratory motion artifacts through a multimodal image fusion module, improves target delineation efficiency by combining deep learning segmentation technology, and dynamically adaptively optimizes the dosage plan in real time. The prognostic model integrates multi-dimensional data for individualized risk prediction.

Benefits of technology

It achieves high-precision target delineation, consistent dose calculation, and dynamic adjustment, reducing the risk of damage to normal tissues and improving the accuracy and individualized treatment effect of radiotherapy.

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Abstract

The invention discloses an AI auxiliary radiotherapy dose intelligent optimization device, which comprises a multi-modal image fusion module, a dose distribution prediction module, a dynamic adaptive optimization module and a prognosis model integration module, and is characterized in that the multi-modal image fusion module is used for aligning anatomical features of different images, eliminating respiratory motion artifacts and predicting the dose distribution of the different images; the dose distribution prediction module is used for rapidly predicting radiotherapy dose distribution based on anatomical features and historical data, the dynamic adaptive optimization module is used for monitoring anatomical changes in real time and dynamically adjusting a dose plan, and the prognosis model integration module is used for quantifying correlation between dose distribution and radioactive injury risks. High-precision alignment and respiratory motion artifact elimination of an anatomical structure are achieved through the multi-modal image fusion module, three-dimensional dose calculation is rapidly and accurately conducted through the dose distribution prediction module, organ displacement and deformation are effectively coped with through the dynamic self-adaptive optimization module through a real-time image monitoring and reinforcement learning algorithm, and the accuracy of the three-dimensional dose calculation is improved. And the prognosis model integration module constructs an individualized risk prediction model.
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